280 citations · 2k across the 81 of their papers we have counts for
4 papers · 2 filters
Memory Efficient Meta-Learning with Large Images
John Bronskill, Daniela Massiceti, Massimiliano Patacchiola +3
Meta learning approaches to few-shot classification are computationally efficient at test time, requiring just a few optimization steps or single forward pass to learn a new task,…
How Tight Can PAC-Bayes be in the Small Data Regime?
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt +1
In this paper, we investigate the question: Given a small number of datapoints, for example N = 30, how tight can PAC-Bayes and test set bounds be made? For such small datasets, te…
Bayesian Neural Network Priors Revisited
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober +5
Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference. However, it is unclear whether these priors accurately reflect our true beliefs ab…
The Gaussian Neural Process
Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong +2
Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes. We provide a rigorous a…